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<p>Active labour market programmes (ALMPs) are central to Morocco’s employment strategy, yet comparative evidence on their relative association with youth labour-market insertion remains scarce. Using harmonised administrative microdata on 4500 beneficiaries (2016–2024), this study compares three flagship programmes IDMAJ (wage subsidies), TAEHIL (vocational training), and FORSA (entrepreneurship and placement support) through binomial logistic regression on pre-treatment characteristics, complemented by propensity score matching and a five-fold cross-validated benchmark against five machine-learning algorithms. Relative to IDMAJ, TAEHIL shows consistently lower insertion odds (OR&#xa0;=&#xa0;0.683, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(p&lt;0.001\)</EquationSource> </InlineEquation>; <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\approx \)</EquationSource> </InlineEquation> 9.7 percentage points), a gap confirmed by matching estimates (ATT&#xa0;=&#xa0;12.7&#xa0;pp); FORSA’s association weakens once post-treatment covariates are included. Secondary-qualifying education is the strongest positive predictor of insertion (OR&#xa0;=&#xa0;1.41), and programme effects concentrate among male beneficiaries. No algorithm outperforms logistic regression (AUC: 0.509-−0.530), indicating that available administrative records lack the informational richness required for individual-level targeting. Findings support intensified post-programme matching assistance and investment in richer demand-side data before predictive targeting tools are deployed.</p>

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Active labour market programme type influences youth employment integration in Morocco

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月26日

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  • Youness Ezzaaime,
  • Sara Haddouche,
  • Ali Raigat,
  • El Amine Rouaga,
  • Said Maizzou,
  • Abdeljabbar Abdouni

摘要

Active labour market programmes (ALMPs) are central to Morocco’s employment strategy, yet comparative evidence on their relative association with youth labour-market insertion remains scarce. Using harmonised administrative microdata on 4500 beneficiaries (2016–2024), this study compares three flagship programmes IDMAJ (wage subsidies), TAEHIL (vocational training), and FORSA (entrepreneurship and placement support) through binomial logistic regression on pre-treatment characteristics, complemented by propensity score matching and a five-fold cross-validated benchmark against five machine-learning algorithms. Relative to IDMAJ, TAEHIL shows consistently lower insertion odds (OR = 0.683, \(p<0.001\) ; \(\approx \) 9.7 percentage points), a gap confirmed by matching estimates (ATT = 12.7 pp); FORSA’s association weakens once post-treatment covariates are included. Secondary-qualifying education is the strongest positive predictor of insertion (OR = 1.41), and programme effects concentrate among male beneficiaries. No algorithm outperforms logistic regression (AUC: 0.509-−0.530), indicating that available administrative records lack the informational richness required for individual-level targeting. Findings support intensified post-programme matching assistance and investment in richer demand-side data before predictive targeting tools are deployed.